# Combine TreeSummarizedExperiment objects

# Combine multiple `TreeSummarizedExperiment` objects

Multiple `TreeSummarizedExperiemnt` objects (TSE) can be combined by using `rbind` or `cbind`. Here, we create a toy `TreeSummarizedExperiment` object using `makeTSE()` (see `?makeTSE()`). As the tree in the row/column tree slot is generated randomly using `ape::rtree()`, `set.seed()` is used to create reproducible results.

    library(TreeSummarizedExperiment)

    set.seed(1)
    # TSE: without the column tree
    (tse_a <- makeTSE(include.colTree = FALSE))

    ## class: TreeSummarizedExperiment
    ## dim: 10 4
    ## metadata(0):
    ## assays(1): ''
    ## rownames(10): entity1 entity2 ... entity9 entity10
    ## rowData names(2): var1 var2
    ## colnames(4): sample1 sample2 sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (10 rows)
    ## rowTree: 1 phylo tree(s) (10 leaves)
    ## colLinks: NULL
    ## colTree: NULL

    # combine two TSEs by row
    (tse_aa <- rbind(tse_a, tse_a))

    ## class: TreeSummarizedExperiment
    ## dim: 20 4
    ## metadata(0):
    ## assays(1): ''
    ## rownames(20): entity1 entity2 ... entity9 entity10
    ## rowData names(2): var1 var2
    ## colnames(4): sample1 sample2 sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (20 rows)
    ## rowTree: 1 phylo tree(s) (10 leaves)
    ## colLinks: NULL
    ## colTree: NULL

The generated `tse_aa` has 20 rows, which is two times of that in `tse_a`. The row tree in `tse_aa` is the same as that in `tse_a`.

    identical(rowTree(tse_aa), rowTree(tse_a))

    ## [1] TRUE

If we `rbind` two TSEs (e.g., `tse_a` and `tse_b`) that have different row trees, the obtained TSE (e.g., `tse_ab`) will have two row trees.

    set.seed(2)
    tse_b <- makeTSE(include.colTree = FALSE)

    # different row trees
    identical(rowTree(tse_a), rowTree(tse_b))

    ## [1] FALSE

    # 2 phylo tree(s) in rowTree
    (tse_ab <- rbind(tse_a, tse_b))

    ## class: TreeSummarizedExperiment
    ## dim: 20 4
    ## metadata(0):
    ## assays(1): ''
    ## rownames(20): entity1 entity2 ... entity9 entity10
    ## rowData names(2): var1 var2
    ## colnames(4): sample1 sample2 sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (20 rows)
    ## rowTree: 2 phylo tree(s) (20 leaves)
    ## colLinks: NULL
    ## colTree: NULL

In the row link data, the `whichTree` column gives information about which tree the row is mapped to. For `tse_aa`, there is only one tree named as `phylo`. However, for `tse_ab`, there are two trees (`phylo` and `phylo.1`).

    rowLinks(tse_aa)

    ## LinkDataFrame with 20 rows and 5 columns
    ##              nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##          <character>   <character> <integer> <logical> <character>
    ## entity1      entity1       alias_1         1      TRUE       phylo
    ## entity2      entity2       alias_2         2      TRUE       phylo
    ## entity3      entity3       alias_3         3      TRUE       phylo
    ## entity4      entity4       alias_4         4      TRUE       phylo
    ## entity5      entity5       alias_5         5      TRUE       phylo
    ## ...              ...           ...       ...       ...         ...
    ## entity6      entity6       alias_6         6      TRUE       phylo
    ## entity7      entity7       alias_7         7      TRUE       phylo
    ## entity8      entity8       alias_8         8      TRUE       phylo
    ## entity9      entity9       alias_9         9      TRUE       phylo
    ## entity10    entity10      alias_10        10      TRUE       phylo

    rowLinks(tse_ab)

    ## LinkDataFrame with 20 rows and 5 columns
    ##              nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##          <character>   <character> <integer> <logical> <character>
    ## entity1      entity1       alias_1         1      TRUE       phylo
    ## entity2      entity2       alias_2         2      TRUE       phylo
    ## entity3      entity3       alias_3         3      TRUE       phylo
    ## entity4      entity4       alias_4         4      TRUE       phylo
    ## entity5      entity5       alias_5         5      TRUE       phylo
    ## ...              ...           ...       ...       ...         ...
    ## entity6      entity6       alias_6         6      TRUE     phylo.1
    ## entity7      entity7       alias_7         7      TRUE     phylo.1
    ## entity8      entity8       alias_8         8      TRUE     phylo.1
    ## entity9      entity9       alias_9         9      TRUE     phylo.1
    ## entity10    entity10      alias_10        10      TRUE     phylo.1

The name of trees can be accessed using `rowTreeNames`. If the input TSEs use the same name for trees, `rbind` will automatically create valid and unique names for trees by using `make.names`. `tse_a` and `tse_b` both use `phylo` as the name of their row trees. In `tse_ab`, the row tree that originates from `tse_b` is named as `phylo.1` instead.

    rowTreeNames(tse_aa)

    ## [1] "phylo"

    rowTreeNames(tse_ab)

    ## [1] "phylo"   "phylo.1"

    # The original tree names in the input TSEs
    rowTreeNames(tse_a)

    ## [1] "phylo"

    rowTreeNames(tse_b)

    ## [1] "phylo"

Once the name of trees is changed, the column `whichTree` in the `rowLinks()` is updated accordingly.

    rowTreeNames(tse_ab) <- paste0("tree", 1:2)
    rowLinks(tse_ab)

    ## LinkDataFrame with 20 rows and 5 columns
    ##              nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##          <character>   <character> <integer> <logical> <character>
    ## entity1      entity1       alias_1         1      TRUE       tree1
    ## entity2      entity2       alias_2         2      TRUE       tree1
    ## entity3      entity3       alias_3         3      TRUE       tree1
    ## entity4      entity4       alias_4         4      TRUE       tree1
    ## entity5      entity5       alias_5         5      TRUE       tree1
    ## ...              ...           ...       ...       ...         ...
    ## entity6      entity6       alias_6         6      TRUE       tree2
    ## entity7      entity7       alias_7         7      TRUE       tree2
    ## entity8      entity8       alias_8         8      TRUE       tree2
    ## entity9      entity9       alias_9         9      TRUE       tree2
    ## entity10    entity10      alias_10        10      TRUE       tree2

To run `cbind`, TSEs should agree in the row dimension. If TSEs only differ in the row tree, the row tree and the row link data are dropped.

    cbind(tse_a, tse_a)

    ## class: TreeSummarizedExperiment
    ## dim: 10 8
    ## metadata(0):
    ## assays(1): ''
    ## rownames(10): entity1 entity2 ... entity9 entity10
    ## rowData names(2): var1 var2
    ## colnames(8): sample1 sample2 ... sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (10 rows)
    ## rowTree: 1 phylo tree(s) (10 leaves)
    ## colLinks: NULL
    ## colTree: NULL

    cbind(tse_a, tse_b)

    ## Warning in cbind(...): rowTree & rowLinks differ in the provided TSEs.
    ##  rowTree & rowLinks are dropped after 'cbind'

    ## class: TreeSummarizedExperiment
    ## dim: 10 8
    ## metadata(0):
    ## assays(1): ''
    ## rownames(10): entity1 entity2 ... entity9 entity10
    ## rowData names(2): var1 var2
    ## colnames(8): sample1 sample2 ... sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (10 rows)
    ## rowTree: 1 phylo tree(s) (10 leaves)
    ## colLinks: NULL
    ## colTree: NULL

# Subset a TSE object

We obtain a subset of `tse_ab` by extracting the data on rows `11:15`. These rows are mapped to the same tree named as `phylo.1`. So, the `rowTree` slot of `sse` has only one tree.

    (sse <- tse_ab[11:15, ])

    ## class: TreeSummarizedExperiment
    ## dim: 5 4
    ## metadata(0):
    ## assays(1): ''
    ## rownames(5): entity1 entity2 entity3 entity4 entity5
    ## rowData names(2): var1 var2
    ## colnames(4): sample1 sample2 sample3 sample4
    ## colData names(2): ID group
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ## rowLinks: a LinkDataFrame (5 rows)
    ## rowTree: 1 phylo tree(s) (10 leaves)
    ## colLinks: NULL
    ## colTree: NULL

    rowLinks(sse)

    ## LinkDataFrame with 5 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity1     entity1       alias_1         1      TRUE       tree2
    ## entity2     entity2       alias_2         2      TRUE       tree2
    ## entity3     entity3       alias_3         3      TRUE       tree2
    ## entity4     entity4       alias_4         4      TRUE       tree2
    ## entity5     entity5       alias_5         5      TRUE       tree2

`[` works not only as a getter but also a setter to replace a subset of `sse`.

    set.seed(3)
    tse_c <- makeTSE(include.colTree = FALSE)
    rowTreeNames(tse_c) <- "new_tree"

    # the first two rows are from tse_c, and are mapped to 'new_tree'
    sse[1:2, ] <- tse_c[5:6, ]
    rowLinks(sse)

    ## LinkDataFrame with 5 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_5         5      TRUE    new_tree
    ## entity6     entity6       alias_6         6      TRUE    new_tree
    ## entity3     entity3       alias_3         3      TRUE       tree2
    ## entity4     entity4       alias_4         4      TRUE       tree2
    ## entity5     entity5       alias_5         5      TRUE       tree2

The TSE object can be subset also by nodes or/and trees using `subsetByNodes`

    # by tree
    sse_a <- subsetByNode(x = sse, whichRowTree = "new_tree")
    rowLinks(sse_a)

    ## LinkDataFrame with 2 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_5         5      TRUE    new_tree
    ## entity6     entity6       alias_6         6      TRUE    new_tree

    # by node
    sse_b <- subsetByNode(x = sse, rowNode = 5)
    rowLinks(sse_b)

    ## LinkDataFrame with 2 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_5         5      TRUE    new_tree
    ## entity5     entity5       alias_5         5      TRUE       tree2

    # by tree and node
    sse_c <- subsetByNode(x = sse, rowNode = 5, whichRowTree = "tree2")
    rowLinks(sse_c)

    ## LinkDataFrame with 1 row and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_5         5      TRUE       tree2

# Change specific trees of TSE

By using `colTree`, we can add a column tree to `sse` that has no column tree before.

    colTree(sse)

    ## NULL

    library(ape)
    set.seed(1)
    col_tree <- rtree(ncol(sse))

    # To use 'colTree` as a setter, the input tree should have node labels matching
    # with column names of the TSE.
    col_tree$tip.label <- colnames(sse)

    colTree(sse) <- col_tree
    colTree(sse)

    ##
    ## Phylogenetic tree with 4 tips and 3 internal nodes.
    ##
    ## Tip labels:
    ##   sample1, sample2, sample3, sample4
    ##
    ## Rooted; includes branch length(s).

`sse` has two row trees. We can replace one of them with a new tree by specifying `whichTree` of the `rowTree`.

    # the original row links
    rowLinks(sse)

    ## LinkDataFrame with 5 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_5         5      TRUE    new_tree
    ## entity6     entity6       alias_6         6      TRUE    new_tree
    ## entity3     entity3       alias_3         3      TRUE       tree2
    ## entity4     entity4       alias_4         4      TRUE       tree2
    ## entity5     entity5       alias_5         5      TRUE       tree2

    # the new row tree
    set.seed(1)
    row_tree <- rtree(4)
    row_tree$tip.label <- paste0("entity", 5:7)

    # replace the tree named as the 'new_tree'
    nse <- sse
    rowTree(nse, whichTree = "new_tree") <- row_tree
    rowLinks(nse)

    ## LinkDataFrame with 5 rows and 5 columns
    ##             nodeLab nodeLab_alias   nodeNum    isLeaf   whichTree
    ##         <character>   <character> <integer> <logical> <character>
    ## entity5     entity5       alias_1         1      TRUE    new_tree
    ## entity6     entity6       alias_2         2      TRUE    new_tree
    ## entity3     entity3       alias_3         3      TRUE       tree2
    ## entity4     entity4       alias_4         4      TRUE       tree2
    ## entity5     entity5       alias_5         5      TRUE       tree2

In the row links, the first two rows now have new values in `nodeNum` and `nodeLab_alias`. The name in `whichTree` is not changed but the tree is actually updated.

    # FALSE is expected
    identical(rowTree(sse, whichTree = "new_tree"),
              rowTree(nse, whichTree = "new_tree"))

    ## [1] FALSE

    # TRUE is expected
    identical(rowTree(nse, whichTree = "new_tree"),
              row_tree)

    ## [1] TRUE

If nodes of the input tree and rows of the TSE are named differently, users can match rows with nodes via `changeTree` with `rowNodeLab` provided.

# Session Info

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] ggplot2_4.0.2                   ggtree_4.0.4
    ##  [3] ape_5.8-1                       TreeSummarizedExperiment_2.18.0
    ##  [5] Biostrings_2.78.0               XVector_0.50.0
    ##  [7] SingleCellExperiment_1.32.0     SummarizedExperiment_1.40.0
    ##  [9] Biobase_2.70.0                  GenomicRanges_1.62.1
    ## [11] Seqinfo_1.0.0                   IRanges_2.44.0
    ## [13] S4Vectors_0.48.0                BiocGenerics_0.56.0
    ## [15] generics_0.1.4                  MatrixGenerics_1.22.0
    ## [17] matrixStats_1.5.0               BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] ggiraph_0.9.6           tidyselect_1.2.1        dplyr_1.2.0
    ##  [4] farver_2.1.2            S7_0.2.1                fastmap_1.2.0
    ##  [7] lazyeval_0.2.2          fontquiver_0.2.1        digest_0.6.39
    ## [10] lifecycle_1.0.5         tidytree_0.4.7          magrittr_2.0.4
    ## [13] compiler_4.5.2          rlang_1.1.7             sass_0.4.10
    ## [16] tools_4.5.2             yaml_2.3.12             knitr_1.51
    ## [19] S4Arrays_1.10.1         labeling_0.4.3          htmlwidgets_1.6.4
    ## [22] DelayedArray_0.36.0     RColorBrewer_1.1-3      aplot_0.2.9
    ## [25] abind_1.4-8             BiocParallel_1.44.0     withr_3.0.2
    ## [28] purrr_1.2.1             sys_3.4.3               grid_4.5.2
    ## [31] gdtools_0.5.0           scales_1.4.0            MASS_7.3-65
    ## [34] cli_3.6.5               rmarkdown_2.30          crayon_1.5.3
    ## [37] treeio_1.34.0           cachem_1.1.0            parallel_4.5.2
    ## [40] ggplotify_0.1.3         BiocManager_1.30.27     yulab.utils_0.2.4
    ## [43] vctrs_0.7.1             Matrix_1.7-4            jsonlite_2.0.0
    ## [46] fontBitstreamVera_0.1.1 gridGraphics_0.5-1      patchwork_1.3.2
    ## [49] systemfonts_1.3.1       maketools_1.3.2         tidyr_1.3.2
    ## [52] jquerylib_0.1.4         glue_1.8.0              codetools_0.2-20
    ## [55] gtable_0.3.6            tibble_3.3.1            pillar_1.11.1
    ## [58] rappdirs_0.3.4          htmltools_0.5.9         R6_2.6.1
    ## [61] evaluate_1.0.5          lattice_0.22-9          ggfun_0.2.0
    ## [64] fontLiberation_0.1.0    bslib_0.10.0            Rcpp_1.1.1
    ## [67] SparseArray_1.10.8      nlme_3.1-168            xfun_0.56
    ## [70] fs_1.6.6                buildtools_1.0.0        pkgconfig_2.0.3

# Reference